Artificial Intelligence (AI) has gone from being a simple technological trend to becoming a crucial tool that is transforming industries globally. In the cryptocurrency, blockchain and DeFi (decentralized finance) sectors, AI has established itself as a driver of innovation, offering solutions that improve efficiency and precision in decision-making, as well as the way information is analyzed and used.
The amount of data generated in the DeFi ecosystem is immense and growing exponentially. According to information from DeFiLlama , the total value locked (TVL) in DeFi protocols reached more than $50 billion in 2023, and with it, the amount of data associated with transactions, liquidity, and market behavior has also increased.
Given this scenario, AI is presented as the ideal tool to process and analyze this data quickly and effectively. Through Machine Learning algorithms, patterns and trends can be identified in real time, something that would be practically impossible for the human eye.
Dune Analytics and Nansen already use AI for detailed on-chain analytics, providing insights into large whale portfolios and market trends.
In fact, tools like DeFiLlama which is widely used to track the TVL of various DeFi platforms, can be integrated with AI plugins to offer deeper analytics, helping investors identify opportunities in liquidity pools that offer higher returns and better manage the risk of exposure to volatile assets.
Trading in DeFi is a process that can be very complex and volatile due to the high price fluctuation and speed at which transactions occur. AI has revolutionized this field by introducing automated trading strategies that adapt and learn from market changes in real-time.
Trading bots, such as those offered by Auton.io are capable of executing transactions in milliseconds, allowing them to take advantage of arbitrage opportunities and minimize losses in the face of volatility.
AI-based trading models have demonstrated their ability to outperform traditional strategies. For example, a study titled " Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach. " found that strategies incorporating Long Short-term Memory (LSTM) models outperform traditional strategies, providing a significant advantage in market prediction and trading decision making.
Additionally, another Telescopia report highlights how AI-based predictive trading models can analyze large amounts of data and employ high-frequency trading algorithms to achieve higher returns on investment, outperforming traditional methods.
This shows that AI-based trading strategies offer a competitive advantage by better adapting to market changes and improving decision-making efficiency.
In the realm of Dollar-Cost Averaging ( DCA ) strategies AI also plays a key role by automatically adjusting periodic asset purchases based on market trends. This helps investors mitigate the impact of volatility and optimize their long-term returns, even in bear markets.
One of the biggest concerns in the DeFi ecosystem is the risk associated with the investment. With the increasing number of attacks and exploits on DeFi protocols, which have caused losses worth more than $3.5 billion in 2022 according to Cointelegraphrisk management has become a priority for investors and developers.
AI offers risk management tools that analyze variables such as market volatility, regulatory changes and user behavior.
For example according to a report by Ledger Works, a company specializing in risk management and data analytics solutions for the blockchain and DeFi sector, AI and machine learning are being used for risk management in DeFi. Its inference models allow it to analyze real-time data, both on-chain and off-chain, and apply techniques such as Monte Carlo simulation to optimize rates on DEXs and dynamically adjust liquidity providers' fees, helping to manage risks and maximize returns.
These models also apply to the security of liquidity pools, where AI can monitor activity in real time, detect anomalous behavior and prevent potential attacks or exploits. This contributes to a more secure environment for those participating in the provision of liquidity or transacting on DeFi protocols.
AI is being used to audit and optimize the execution of smart contracts. Tools such as natural language parsing(NLP) can review documentation and audit reports to identify common coding errors prior to smart contract deployment, such as buffer overflows and re-entry problems. In addition, AI can optimize the execution of smart contracts, enabling more efficient transactions in decentralized applications.
Tools such as ConsenSys Diligence offer advanced solutions for analyzing smart contract code and detecting potential vulnerabilities prior to implementation on the blockchain. Although not AI-based, they use static and dynamic analysis techniques, along with fuzzing, to identify and correct security issues, which significantly reduces the risk of bugs and fraud, providing a more secure environment for DeFi users.
The synergy between AI and the crypto world has led to the creation of several platforms that offer innovative solutions. Below, we present some prominent examples:
It is a decentralized hedge fund that uses AI and blockchain technology to make predictions about the financial market. Numerai allows data scientists from around the world to participate by submitting their predictive models, and in return, they are rewarded with the NMR native token. This encourages the creation of high-quality AI models that help improve accuracy in fund investment decisions.
It is a decentralized marketplace platform that allows developers to buy and sell AI services using blockchain technology. Available services range from data analytics algorithms to natural language processing models. SingularityNET is driving the democratization of AI, enabling businesses and individuals to access AI solutions more easily and transparently through the use of the AGIX token.
It is a platform that offers AI-based automated trading tools for cryptocurrency investors. Auton.io provides trading bots that use AI algorithms to analyze markets and execute trades automatically, taking advantage of trading opportunities more effectively. Users can customize their strategies and use the NIOX token to access premium features within the platform.
This platform uses blockchain technology to create an ecosystem of autonomous AI-powered agents that can perform complex tasks on behalf of users. These agents can collect and analyze market data, perform transactions and optimize trading processes in DeFi. Fetch.ai is designed to improve trading efficiency in multiple sectors, including logistics, energy and, of course, decentralized finance, using the FET token as the medium of exchange within the network.
It is a decentralized network that combines artificial intelligence and blockchain technology, allowing developers and participants to collaboratively train and share AI models. Bittensor uses an incentive mechanism based on the TAO token, which rewards users who contribute computational resources and knowledge to the network.
To better understand the impact and differences in the use of AI in the world of cryptocurrencies and DeFi, it is useful to compare it with its application in the traditional financial sector. While in both cases AI plays a crucial role, the way it is implemented and the benefits it offers can vary significantly.
Appearance |
AI in DeFi and Crypto |
AI in Traditional Finance |
|---|---|---|
Transparency |
High transparency thanks to blockchain technology; data is accessible and auditable by any user. | Limited, as most operations and decisions are hidden from the public and controlled by centralized institutions. |
Data Access |
Access to real-time data of all transactions on the blockchain. | The data is often private and requires authorization. |
Decentralization |
Implementation in a decentralized environment, allows open participation. Example: Bittensor. | Highly centralized, controlled by financial institutions. |
Execution Speed |
Very fast thanks to the blockchain infrastructure. | Depends on the infrastructure of the bank or institution. |
Risk Management |
Uses AI to identify vulnerabilities and adjust strategies. | It uses AI primarily for predictive analytics. |
Transaction Costs |
Transaction costs can be lower, especially on efficient blockchains, but vary depending on network congestion. | Costs are usually higher due to the involvement of multiple intermediaries (brokers, banks, etc.) in each financial transaction. |
Flexibility and Adaptability |
The AI algorithms in DeFi can adapt quickly to market changes, and users have more control to adjust their strategies. | In traditional finance, AI systems tend to be less flexible and adaptable, as they are embedded in more rigid and regulated infrastructures. |
Interaction with Users |
DeFi platforms such as SingularityNET enable direct interaction between users and AI service providers, without intermediaries. | Interaction is limited; users often have to deal with the financial institution to access services using AI. |
Use of Tokens |
AI is integrated with native tokens that facilitate access and sharing of AI services. Example: SingularityNET's AGIX. | There is no direct equivalent to tokens; transactions and payments are made in traditional fiat currencies. |
Global and Democratic Access |
AI in DeFi is available to anyone with Internet access, democratizing its use and contribution. | Access restricted and controlled by institutions, limiting participation to those within the traditional financial system. |
While AI is revolutionizing the world of decentralized finance, there is also some misinformation about what it can actually achieve. To take full advantage of its potential, it is important to recognize what is possible and what is not. The following myths reflect misconceptions that have arisen in both DeFi and traditional finance.
A common misconception is that AI can operate without any human intervention or oversight. The reality is that AI systems require constant training, supervision, and fine-tuning by experts. Human decision-making remains essential, especially in markets as volatile as DeFi, where intuition and context play a key role.
Despite its power to streamline processes and analytics, AI is not a silver bullet. Problems like security, scalability, and DeFi adoption require solutions that go beyond what AI can offer. Trying to apply AI to every aspect of DeFi without a clear purpose can be ineffective and, in some cases, even harmful.
It's easy to think that AI algorithms will always outperform human traders. However, even the most advanced systems face the same market uncertainties and risks. Outcomes are not always predictable, and many automated trading systems still need constant tweaking and monitoring to remain profitable.
While AI can help detect fraudulent behavior and automate processes, it cannot completely replace the need to investigate and validate the trustworthiness of projects, teams, and protocols. Trust is a fundamental pillar in DeFi, and AI, while it can help reinforce it, does not eliminate the need for careful analysis and due diligence.
Despite the numerous benefits that AI brings to the DeFi and blockchain sector, there are still significant challenges that need to be addressed. One of them is the over-reliance on algorithms, which can lead to decisions being made based solely on historical data and patterns that will not necessarily be repeated in the future.
Furthermore, transparency and traceability of AI algorithms is a crucial issue, as it is essential to ensure that automated decisions are fair and not influenced by bias. However, the future is promising, and the combination of AI and blockchain can lead to the creation of even more decentralized, efficient and secure financial systems.
